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Genomic prediction in CIMMYT maize and wheat breeding programs.

Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K - Heredity (Edinb) (2013)

Bottom Line: However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible.When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments.Further research on the quantification of breeding value components for GS in plant breeding populations is required.

View Article: PubMed Central - PubMed

Affiliation: Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.

ABSTRACT
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

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Related in: MedlinePlus

Heat map of the G matrix for the data set with 599 wheat lines genotyped with 1279 DArTs markers.
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fig2: Heat map of the G matrix for the data set with 599 wheat lines genotyped with 1279 DArTs markers.

Mentions: The summary in Table 2 shows the prediction ability of two wheat data sets for grain yield measured in various environments. Here, the number of genotypes in each data set is 306 and 599, and the number of markers is 1717 and 1279, respectively (Pérez et al., 2012; Burgueño et al., 2012). Prediction accuracies were highly consistent in both sets of experiments, with BL with markers and pedigree (PMBL), and RKHS with pedigree and markers (PMRKHS) giving the best predictions on the data set of 599 wheat lines and with models MRKHS and MRBFNN as the best predictors on the data set of 306 wheat lines. Figures 1 and 2 depict the heatmap of the genomic (G) matrix for data on 306 and 599 wheat lines, respectively. The 306 lines comprise three groups, one large population (top left), one small subset unrelated to either of the other two and another large population that is closely related to the first large one, apparently with two closely related subgroups (Figure 1). The 599 wheat lines formed two clear large groups, each with several subgroups closely related to each other (Figure 2); the two large subgroups overlap slightly.


Genomic prediction in CIMMYT maize and wheat breeding programs.

Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K - Heredity (Edinb) (2013)

Heat map of the G matrix for the data set with 599 wheat lines genotyped with 1279 DArTs markers.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860161&req=5

fig2: Heat map of the G matrix for the data set with 599 wheat lines genotyped with 1279 DArTs markers.
Mentions: The summary in Table 2 shows the prediction ability of two wheat data sets for grain yield measured in various environments. Here, the number of genotypes in each data set is 306 and 599, and the number of markers is 1717 and 1279, respectively (Pérez et al., 2012; Burgueño et al., 2012). Prediction accuracies were highly consistent in both sets of experiments, with BL with markers and pedigree (PMBL), and RKHS with pedigree and markers (PMRKHS) giving the best predictions on the data set of 599 wheat lines and with models MRKHS and MRBFNN as the best predictors on the data set of 306 wheat lines. Figures 1 and 2 depict the heatmap of the genomic (G) matrix for data on 306 and 599 wheat lines, respectively. The 306 lines comprise three groups, one large population (top left), one small subset unrelated to either of the other two and another large population that is closely related to the first large one, apparently with two closely related subgroups (Figure 1). The 599 wheat lines formed two clear large groups, each with several subgroups closely related to each other (Figure 2); the two large subgroups overlap slightly.

Bottom Line: However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible.When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments.Further research on the quantification of breeding value components for GS in plant breeding populations is required.

View Article: PubMed Central - PubMed

Affiliation: Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.

ABSTRACT
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

Show MeSH
Related in: MedlinePlus